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Traffic flow forecasting is an important application of computational intelligence and an active research topic in Intelligent Transportation Systems (ITS). However, traditional methods called single-link traffic flow forecasting usually predict one link's unidirectional traffic flow at a time, which do not take the relevance of adjacent links into account and make the ITS have a low efficiency. In...
Ensemble learning plays an important role in pattern recognition. It combines multiple generated models to solve learning problems, such as classification, regression and feature selection. Existing ensemble methods combine classifiers which are generated and run in parallel. In this paper, a novel ensemble learning approach for semi-supervised learning is proposed. Different from existing ensemble...
Biometrics based on electroencephalogram (EEG) signals is an emerging research topic. Several recent results have shown its feasibility and potential for personal identification. However, they all use a single task (e.g., signals recorded during imagination of repetitive left hand movements or during resting with eyes open) for classifier design and subsequent identification. In contrast with this,...
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